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COVID-19 VACCINATION ANALYSIS

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Table of Contents:

• About the Data
• Importing Libraries
• Reading the Data
• Cleaning the Data
• Exploratory Data Analysis
  • Basic Info about Dataset
  • Amount of Vaccinated People
  • Country wise Daily vaccination
  • Percent of Population Vaccinated
  • People vaccinated once VS People vaccinated twice
  • Most Popular Vaccine Scheme
  • Market share of Vaccine Schemes
  • TreeMap of Total Vaccinations per country, grouped by Vaccine Scheme
  • Visualising on a Map
  • Animating the Vaccination Progress
• Conclusion

About the Data:

This Notebook would not have been possible without this Dataset provided by @Gabriel Preda.

The Data contains the following information:

• Country - this is the country for which the vaccination information is provided.
• Country ISO Code - ISO code for the country.
• Date - date for the data entry; for some dates we have only the daily vaccinations, for others, only the (cumulative) total.
• Total number of vaccinations - this is the absolute number of total immunizations in the country.
• Total number of people vaccinated - a person, depending on the immunization scheme, will receive one or more (typically 2) vaccines; at a certain moment, the number of vaccination might be larger than the number of people.
• Total number of people fully vaccinated - this is the number of people that received the entire set of immunization according to the immunization scheme (typically 2); at a certain moment in time, there might be a certain number of people that received one vaccine and another number (smaller) of people that received all vaccines in the scheme.
• Daily vaccinations(raw) - for a certain data entry, the number of vaccination for that date/country.
• Daily vaccinations - for a certain data entry, the number of vaccination for that date/country.
• Total vaccinations per hundred - ratio (in percent) between vaccination number and total population up to the date in the country.
• Total number of people vaccinated per hundred - ratio (in percent) between population immunized and total population up to the date in the country.
• Total number of people fully vaccinated per hundred - ratio (in percent) between population fully immunized and total population up to the date in the country.
• Number of vaccinations per day - number of daily vaccination for that day and country.
• Daily vaccinations per million - ratio (in ppm) between vaccination number and total population for the current date in the country.
• Vaccines used in the country - total number of vaccines used in the country (up to date).
• Source name - source of the information (national authority, international organization, local organization etc.).
• Source website - website of the source of information.

Importing Libraries:

We initialize the Python packages we will use for data ingestion, preparation and visualization. We will use :

• Pandas to read and process the data.
• Plotly for visualization.

import pandas as pd
import plotly.express as px
import plotly.graph_objects as go

Reading the Data:

df = pd.read_csv('country_vaccinations.csv')
df.head()

	country	iso_code	date	total_vaccinations	people_vaccinated	people_fully_vaccinated	daily_vaccinations_raw	daily_vaccinations	total_vaccinations_per_hundred	people_vaccinated_per_hundred	people_fully_vaccinated_per_hundred	daily_vaccinations_per_million	vaccines	source_name	source_website
0	Albania	ALB	2021-01-10	0.0	0.0	NaN	NaN	NaN	0.00	0.00	NaN	NaN	Pfizer/BioNTech	Ministry of Health	https://shendetesia.gov.al/covid19-ministria-e...
1	Albania	ALB	2021-01-11	NaN	NaN	NaN	NaN	64.0	NaN	NaN	NaN	22.0	Pfizer/BioNTech	Ministry of Health	https://shendetesia.gov.al/covid19-ministria-e...
2	Albania	ALB	2021-01-12	128.0	128.0	NaN	NaN	64.0	0.00	0.00	NaN	22.0	Pfizer/BioNTech	Ministry of Health	https://shendetesia.gov.al/covid19-ministria-e...
3	Albania	ALB	2021-01-13	188.0	188.0	NaN	60.0	63.0	0.01	0.01	NaN	22.0	Pfizer/BioNTech	Ministry of Health	https://shendetesia.gov.al/covid19-ministria-e...
4	Albania	ALB	2021-01-14	266.0	266.0	NaN	78.0	66.0	0.01	0.01	NaN	23.0	Pfizer/BioNTech	Ministry of Health	https://shendetesia.gov.al/covid19-ministria-e...

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4435 entries, 0 to 4434
Data columns (total 15 columns):
 #   Column                               Non-Null Count  Dtype  
---  ------                               --------------  -----  
 0   country                              4435 non-null   object 
 1   iso_code                             4131 non-null   object 
 2   date                                 4435 non-null   object 
 3   total_vaccinations                   2916 non-null   float64
 4   people_vaccinated                    2483 non-null   float64
 5   people_fully_vaccinated              1662 non-null   float64
 6   daily_vaccinations_raw               2467 non-null   float64
 7   daily_vaccinations                   4281 non-null   float64
 8   total_vaccinations_per_hundred       2916 non-null   float64
 9   people_vaccinated_per_hundred        2483 non-null   float64
 10  people_fully_vaccinated_per_hundred  1662 non-null   float64
 11  daily_vaccinations_per_million       4281 non-null   float64
 12  vaccines                             4435 non-null   object 
 13  source_name                          4435 non-null   object 
 14  source_website                       4435 non-null   object 
dtypes: float64(9), object(6)
memory usage: 519.9+ KB

Cleaning the Data:

As you can see, a lot of null values are present in the data. This may be because:

1. The vaccination drive has just started and will take some time for all the countries to catch up.
2. This Data is collected daily from Our World in Data GitHub repository for Covid-19, and thus during the creation of this dataset some in consistencies may have crept in.

There may be other reasons but let's move forward with cleaning the data and filling the missing values.

df.isnull().sum()

country                                   0
iso_code                                304
date                                      0
total_vaccinations                     1519
people_vaccinated                      1952
people_fully_vaccinated                2773
daily_vaccinations_raw                 1968
daily_vaccinations                      154
total_vaccinations_per_hundred         1519
people_vaccinated_per_hundred          1952
people_fully_vaccinated_per_hundred    2773
daily_vaccinations_per_million          154
vaccines                                  0
source_name                               0
source_website                            0
dtype: int64

df[df['iso_code'].isnull()]['country'].value_counts()

Scotland            76
England             76
Wales               76
Northern Ireland    76
Name: country, dtype: int64

• It seems that the ISO Code for these 4 countries is missing.
• All these countries are part of the United Kingdom.
• As Scotland, England, Northern Ireland and Wales are a part of the United Kingdom, they can be dropped.

df = df.loc[-df.country.isin(['England', 'Scotland', 'Wales', 'Northern Ireland'])]

df[df['iso_code'].isnull()]['country'].value_counts()
#The empty Series output shows that all nan values in iso_code are fixed

Series([], Name: country, dtype: int64)

df.isnull().sum()

country                                   0
iso_code                                  0
date                                      0
total_vaccinations                     1421
people_vaccinated                      1854
people_fully_vaccinated                2663
daily_vaccinations_raw                 1849
daily_vaccinations                      150
total_vaccinations_per_hundred         1421
people_vaccinated_per_hundred          1854
people_fully_vaccinated_per_hundred    2663
daily_vaccinations_per_million          150
vaccines                                  0
source_name                               0
source_website                            0
dtype: int64

df.columns

Index(['country', 'iso_code', 'date', 'total_vaccinations',
       'people_vaccinated', 'people_fully_vaccinated',
       'daily_vaccinations_raw', 'daily_vaccinations',
       'total_vaccinations_per_hundred', 'people_vaccinated_per_hundred',
       'people_fully_vaccinated_per_hundred', 'daily_vaccinations_per_million',
       'vaccines', 'source_name', 'source_website'],
      dtype='object')

Other Apparent Inconsistencies

• total_vaccination is split into people_vaccinated and people_fully_vaccinated and any NaN can be ignored.
• people_fully_vaccinated gives the people who have taken the vaccine twice. Any NaN in this can be ignored
• total_vaccinations_per_hundred is the percent of total vaccinations(i.e. total_vaccinations by the population of the country). Let us rename this
• people_fully_vaccinated_per_hundred is the percent of total vaccinations(i.e. people_fully_vaccinated by the population of the country). Let us rename this
• daily_vaccinations_per_million is the total population of the country by its daily vaccinations. This doesn't have inconsistencies

df.rename(columns = {'total_vaccinations_per_hundred':'total_vaccinations_percent',
                     'people_fully_vaccinated_per_hundred':'people_fully_vaccinated_percent',
                    'people_vaccinated_per_hundred':'people_vaccinated_percent'}, inplace=True)
df.columns

Index(['country', 'iso_code', 'date', 'total_vaccinations',
       'people_vaccinated', 'people_fully_vaccinated',
       'daily_vaccinations_raw', 'daily_vaccinations',
       'total_vaccinations_percent', 'people_vaccinated_percent',
       'people_fully_vaccinated_percent', 'daily_vaccinations_per_million',
       'vaccines', 'source_name', 'source_website'],
      dtype='object')

Exploratory Data Analysis:

Basic Info about Dataset

print('Data point starts from:',df.date.min(),'\n')
print('Data point ends at:',df.date.max(),'\n')
print('Total no of Countries in the data set:',len(df.country.unique()),'\n')
print('Total no of unique Vaccine Schemes in the data set:',len(df.vaccines.unique()),'\n')

Data point starts from: 2020-12-08 

Data point ends at: 2021-02-27 

Total no of Countries in the data set: 108 

Total no of unique Vaccine Schemes in the data set: 20 

# All the different contries
df.country.unique()

array(['Albania', 'Algeria', 'Andorra', 'Anguilla', 'Argentina',
       'Australia', 'Austria', 'Azerbaijan', 'Bahrain', 'Bangladesh',
       'Barbados', 'Belarus', 'Belgium', 'Bermuda', 'Bolivia', 'Brazil',
       'Bulgaria', 'Cambodia', 'Canada', 'Cayman Islands', 'Chile',
       'China', 'Colombia', 'Costa Rica', 'Croatia', 'Cyprus', 'Czechia',
       'Denmark', 'Dominican Republic', 'Ecuador', 'Egypt', 'El Salvador',
       'Estonia', 'Faeroe Islands', 'Falkland Islands', 'Finland',
       'France', 'Germany', 'Gibraltar', 'Greece', 'Greenland',
       'Guernsey', 'Guyana', 'Hungary', 'Iceland', 'India', 'Indonesia',
       'Iran', 'Ireland', 'Isle of Man', 'Israel', 'Italy', 'Japan',
       'Jersey', 'Kazakhstan', 'Kuwait', 'Latvia', 'Lebanon',
       'Liechtenstein', 'Lithuania', 'Luxembourg', 'Macao', 'Maldives',
       'Malta', 'Mauritius', 'Mexico', 'Monaco', 'Montenegro', 'Morocco',
       'Myanmar', 'Nepal', 'Netherlands', 'New Zealand',
       'Northern Cyprus', 'Norway', 'Oman', 'Pakistan', 'Panama',
       'Paraguay', 'Peru', 'Poland', 'Portugal', 'Qatar', 'Romania',
       'Russia', 'Saint Helena', 'Saudi Arabia', 'Senegal', 'Serbia',
       'Seychelles', 'Singapore', 'Slovakia', 'Slovenia', 'South Africa',
       'South Korea', 'Spain', 'Sri Lanka', 'Sweden', 'Switzerland',
       'Trinidad and Tobago', 'Turkey', 'Turks and Caicos Islands',
       'Ukraine', 'United Arab Emirates', 'United Kingdom',
       'United States', 'Venezuela', 'Zimbabwe'], dtype=object)

# All the different kinds of vaccines
df.vaccines.unique()

array(['Pfizer/BioNTech', 'Sputnik V', 'Oxford/AstraZeneca',
       'Moderna, Oxford/AstraZeneca, Pfizer/BioNTech',
       'Oxford/AstraZeneca, Sputnik V',
       'Pfizer/BioNTech, Sinopharm/Beijing, Sputnik V',
       'Oxford/AstraZeneca, Sinovac', 'Sinopharm/Beijing',
       'Moderna, Pfizer/BioNTech', 'Pfizer/BioNTech, Sinovac',
       'Sinopharm/Beijing, Sinopharm/Wuhan, Sinovac',
       'Moderna, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing, Sputnik V',
       'Covaxin, Oxford/AstraZeneca', 'Sinovac',
       'Oxford/AstraZeneca, Pfizer/BioNTech',
       'Oxford/AstraZeneca, Pfizer/BioNTech, Sputnik V',
       'Oxford/AstraZeneca, Sinopharm/Beijing',
       'Oxford/AstraZeneca, Sinopharm/Beijing, Sputnik V',
       'Johnson&Johnson',
       'Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing, Sinopharm/Wuhan, Sputnik V'],
      dtype=object)

Amount of Vaccinated People

# Here we are creating `country_data` which store basic info about a country, like the vaccine scheme it uses, total 
# vaccinations completed and its percentage with the population

country_data = df.copy()
cols = ['country', 'total_vaccinations', 'iso_code', 'vaccines', 'total_vaccinations_percent']

country_data = country_data[cols].groupby('country').max().sort_values('total_vaccinations', ascending=False)
country_data.reset_index(inplace = True)

country_data.columns = ['Country', 'Total Vaccinations', 'iso_code', 'Vaccines', 'Total Vaccinations Percentage']
country_data

	Country	Total Vaccinations	iso_code	Vaccines	Total Vaccinations Percentage
0	United States	72806180.0	USA	Moderna, Pfizer/BioNTech	21.77
1	China	40520000.0	CHN	Sinopharm/Beijing, Sinopharm/Wuhan, Sinovac	2.82
2	United Kingdom	20450858.0	GBR	Oxford/AstraZeneca, Pfizer/BioNTech	30.13
3	India	14242547.0	IND	Covaxin, Oxford/AstraZeneca	1.03
4	Turkey	8514775.0	TUR	Sinovac	10.10
...	...	...	...	...	...
103	New Zealand	1000.0	NZL	Pfizer/BioNTech	0.02
104	Paraguay	1000.0	PRY	Sputnik V	0.01
105	Trinidad and Tobago	440.0	TTO	Oxford/AstraZeneca	0.03
106	Venezuela	157.0	VEN	Sputnik V	0.00
107	Saint Helena	107.0	SHN	Oxford/AstraZeneca	1.76

108 rows × 5 columns

fig = px.bar(country_data[:50], x = 'Country', y = 'Total Vaccinations', color = 'Total Vaccinations')

fig.update_layout(title = dict(text = 'Vaccinizations World-Wide Comparision', x=0.5, y=0.95))
fig.update_xaxes(title = 'Countries', title_font = dict(size=18, family='Courier', color='crimson'), tickangle=-90)
fig.update_yaxes(title = 'Total Vaccinations', title_font = dict(size=18, family='Courier', color='crimson'))

fig.show()

From the plot, some interesting facts stand out:

• The United States, despite having the highest number of people affected by Covid-19, has the highest number of vaccinated people.
• China, from where the virus started spreading, is at second.
• UK, where we have found a new variant strain of the virus, is right next.
• India, who has been supplying vaccines to the world is at 4th position.
• Following that, we have Israel, UAE, Brazil, Germany and others

Country wise Daily Vaccination

top_countries = ['USA','CHN','GBR','IND','ISR','ARE','BRA','DEU','TUR','ITA','FRA']
fig = px.line(df[df.iso_code.isin(top_countries)], x='date', y='daily_vaccinations', color='country')

fig.update_layout(title = dict(text = 'World-Wide Daily Vaccination Timeline', x=0.5, y=0.95), 
                  legend = dict(title = 'Country', traceorder = 'reversed'))
fig.update_xaxes(title = 'Timeline', title_font = dict(size=18, family='Courier', color='crimson'))
fig.update_yaxes(title = 'Daily Vaccinations', title_font = dict(size=18, family='Courier', color='crimson'))

fig.show()

From the plot, we can deduce:

• The Line plot for China is composed entirely of straight lines. This can be attributed to the CCP which tries to restrict flow of information in and out of China. Thus, information from China usually comes in intervals.
• Comparatively, the plot of vaccinations in the USA is better plotted. We can also see that while the USA was heavily affected by the virus, its vaccination drive is highly effective.
• Others like the UK have a steady increase in Daily Vaccinations and India, while supplying to many countries, maintains a respectable 4th position.

Percent of Population Vaccinated

top_country_data = country_data.sort_values('Total Vaccinations Percentage', ascending=False)[:40]

fig = px.bar(top_country_data, x = 'Country', y = 'Total Vaccinations Percentage', color = 'Total Vaccinations Percentage')

fig.update_layout(title = dict(text = 'Percentage of Vaccinated Population World-Wide Comparision', x=0.5, y=0.95))
fig.update_xaxes(title = 'Countries', title_font = dict(size=18, family='Courier', color='crimson'), tickangle=-90)
fig.update_yaxes(title = 'Percentage of Population Vaccinated', title_font = dict(size=14, family='Courier', color='crimson'))

fig.show()

• Countries like Israel, Gibraltar, UAE and Seychelles have the highest level of vaccinated people per hundred.
• We can see that the vaccination percentage of Gibraltar is at 106.5%. This means that Gibraltar has already completed Phase One of vaccination for its population.
• But one shouldn't forget, that the population of these countries isn't really high, so that might be the reason of such a high statistic indicators.
• United Kingdom has really high results, and that's impressive as it's population is almost 7 times higher than UAE's and Israel's, and what is really incredible, 2016 times higher than Gibraltar's!

People vaccinated once VS People vaccinated twice

# group the df by the date and calculate the sum
vaccinated_df = df.copy()
vaccinated_df = vaccinated_df.groupby('date')[['date', 'people_fully_vaccinated', 'people_vaccinated']].sum()

# reset index is to pop out the date index and make a date colum in its place, and the sort via date
vaccinated_df.reset_index(inplace = True)
vaccinated_df.sort_values('date') 

# plot the values
plot = go.Figure(data=[
    go.Scatter( 
        x = vaccinated_df['date'], 
        y = vaccinated_df['people_vaccinated'], 
        stackgroup='two',
        name = 'People Vaccinated once', 
        marker_color= '#35eb28'),
    
    go.Scatter(
        x = vaccinated_df['date'], 
        y = vaccinated_df['people_fully_vaccinated'], 
        stackgroup='one', 
        name = 'People Vaccinated twice', 
        marker_color= '#c4eb28')
    ]) 
    
plot.update_layout(title = dict(text= 'People vaccinated once vs Fully vaccinated till date', x = 0.5, y = 0.95))
plot.update_layout(legend = dict(orientation = "h", yanchor = "bottom", y = 1.02, xanchor = "right", x = 1))
plot.update_xaxes(title = 'Timeline', title_font = dict(size=18, family='Courier', color='crimson'))
plot.update_yaxes(title = 'Amount of vaccinated people', title_font = dict(size=18, family='Courier', color='crimson'))
    
plot.show()

The above plot shows the World-Wide comparison between the First and Second Dose of the Vaccine

From the plot we can determine:

• The People Vaccinated line has gradual ascent but has peculiar depths in the early days of daily vaccinations. These depths fall on Wednesday and Weekends, and so we can conclude that people don't prefer to vaccinate on Wednesdays and Weekends
• In People Vaccinated fully line, the ascent is gradual but much less than People Vaccinated line.
• We can also notice that People Vaccinated line starts to lift from Dec 19 and People Vaccinated fully line starts to lift from Jan 9. This is because of a gap of 3 weeks between the first and second dose of the vaccine.
• From the collected data we can decipher that it will take some time before Covid-19 can be eradicated as the effectiveness of the vaccine is 95% only 4 weeks after the second dose.

Most Popular Vaccine Scheme

top_vaccine = country_data.copy().groupby('Vaccines').sum().sort_values(by = 'Total Vaccinations',ascending = False)
top_vaccine.reset_index(inplace = True)

fig = px.bar(top_vaccine, x = 'Vaccines', y = 'Total Vaccinations', 
            color = 'Vaccines', color_discrete_sequence = px.colors.sequential.Rainbow_r)

fig.update_layout(height= 575, title = dict(text = 'Total Vaccine per Scheme', x=0.5, y=0.95), 
                  legend_title='Types of Vaccine Scheme')
fig.update_xaxes(title = 'Vaccines', title_font = dict(size=18, family='Courier', color='crimson'), showticklabels = False)
fig.update_yaxes(title = 'Amount of vaccinated people', title_font = dict(size=18, family='Courier', color='crimson'))

fig.show()

Market share of Vaccine Schemes

world_wide_vaccine_use = df.copy().vaccines.value_counts().to_dict()
vaccine_type = {}

for scheme,value in world_wide_vaccine_use.items():
    for name in scheme.split(','):
        vaccine_type[name.strip()] = vaccine_type.get(name.strip(),0) + value

fig = px.pie(values = vaccine_type.values(), names = vaccine_type.keys(),
             color_discrete_sequence = px.colors.sequential.Sunset_r)

fig.update_layout(title = dict(text = 'Market share of Vaccines', x=0.5, y=0.95),
                  legend_title='Types of Vaccines')
fig.show()

From the above charts, we can see that :

• The most used vaccine around the world is Pfizer/BioNTech and the second most used vaccine is Oxford/AstraZeneca.
• Indian Covaxin only has a 0.5% world market share even though it has vaccinated over 14M people. This is because Covaxin is only used in India as of now.

TreeMap of Total Vaccinations per country, grouped by Vaccine Scheme

fig = px.treemap(country_data, path = ['Vaccines', 'Country'], values = 'Total Vaccinations', height = 650,
                custom_data = ['Country', 'Vaccines', 'Total Vaccinations'])

fig.update_layout(title = dict(text = 'Total vaccinations per country, grouped by Vaccine Scheme', x=0.5, y=0.95))
fig.update_traces(hovertemplate = 'Country: %{customdata[0]}<br>Vaccine: %{customdata[1]}<br>Total Vaccinations: %{customdata[2]}')
fig.show()

• From the above Treemap we can realise that a Bar and Pie Plot may often only show a part of the information that can be observed, whereas a Treemap can accurately show the share of a particular vaccine world-wide, the countries that are using the said vaccine and can even show comparisons between all the countries.
• As the Treemap shows so much information at a time, it can help one understand the data much more accurately.

Visualising on a Map

fig = px.choropleth(country_data, locations = 'Country', color = 'Vaccines',
                    hover_data = ['Country', 'Vaccines'], locationmode = "country names", 
                    projection = 'natural earth')

fig.update_layout(legend = dict(title = "Vaccine Scheme", orientation = "h", y=-0.1), showlegend = False)
fig.update_layout(title = dict(text = 'Countries using same Vaccine Scheme', x=0.5, y=0.95),
                 geo = dict(showocean = True, oceancolor = "#7af8ff", showland = True, 
                            landcolor = "white", showlakes = False, showframe = False))

fig.show()

• From the above visualisation, we can see the countries that are using similar vaccinations schemes.

fig = px.choropleth(country_data, locations = 'Country', color = 'Total Vaccinations', 
                    locationmode = 'country names', color_continuous_scale = 'rainbow', 
                    hover_name = 'Country', projection = 'natural earth')

fig.update_layout(title = dict(text = 'Total Vaccinations in every Country', x=0.5, y=0.95), 
                 geo = dict(showocean = True, oceancolor = "#7af8ff", showland = True, 
                            landcolor = "white",showlakes = False, showframe = False))

fig.show()

• In the above visualisation, we can see the countries and the total vaccinations they have done.

Animating the Vaccination Progress

# Lets create a copy of the df, fill all the NaN values with a previous value and sort the values according to date
time_df = df.copy()
time_df.fillna(method='bfill', inplace=True)

time_df['date'] = pd.to_datetime(time_df['date'])
time_df = time_df.sort_values('date', ascending=True)
time_df['date'] = time_df['date'].dt.strftime('%m-%d-%Y')

fig = px.choropleth(time_df, locations = 'country', color = 'daily_vaccinations',
                    locationmode = 'country names', hover_name = 'country', 
                    animation_frame = 'date', projection = 'natural earth', 
                    color_continuous_scale = px.colors.sequential.Plasma)

fig.update_layout(title = dict(text = 'Daily Vaccinations World-Wide Timeline', x=0.47, y=0.95), 
                  geo = dict(showocean = True, oceancolor = "#7af8ff", showland = True, 
                             landcolor = "white", showlakes = False, showframe = False))
fig.show()

• The above animation shows the vaccination progress that has occurred over a time period of about 3 months (13-12-2020 to 27-2-2021)
• With this animation we can compare when a country started its vaccination drive on the global scale and at what rate did it vaccinate its population.

Conclusion:

From our analysis we can conclude the following:

• Though the vaccination drive is going in its full pace but in most of the countries people are not fully vaccinated
• In North America and Europe, Pfizer/BioNTech, Oxford/AstraZeneca and Moderna vaccine scheme is widely used
• The most used vaccine around the world is Pfizer/BioNTech.
• Countries like Israel and Gibraltar have given the first dose of the vaccine to an incredible 90-100% of their population.

With this information we can realise that the Covid-19 vaccination drive is going at an incredible pace. Diseases like Polio and Smallpox, which caused many deaths, took decades and centuries to eradicate. At the current pace, Covid-19 can be eradicated in a period of 2 years.